However, approximately 40% of the population carry these alleles and the majority never develop CD. The major genetic risk heterodimer, HLA-DQ2 and DQ8, is already used clinically to help exclude disease. The majority of coeliac disease (CD) patients are not being properly diagnosed and therefore remain untreated, leading to a greater risk of developing CD-associated complications. Romanos, Jihane Rosén, Anna Kumar, Vinod Trynka, Gosia Franke, Lude Szperl, Agata Gutierrez-Achury, Javier van Diemen, Cleo C Kanninga, Roan Jankipersadsing, Soesma A Steck, Andrea Eisenbarth, Georges van Heel, David A Cukrowska, Bozena Bruno, Valentina Mazzilli, Maria Cristina Núñez, Concepcion Bilbao, Jose Ramon Mearin, M Luisa Barisani, Donatella Rewers, Marian Norris, Jill M Ivarsson, Anneli Boezen, H Marieke Liu, Edwin Wijmenga, Cisca Improving coeliac disease risk prediction by testing non-HLA variants additional to HLA variants. We precompute Envision predictions for every possible single amino acid variant in human, mouse, frog, zebrafish, fruit fly, worm, and yeast proteomes (). Finally, we demonstrate that Envision's performance improves as more large-scale mutagenesis data are incorporated. Envision prediction accuracy is also more consistent across amino acids than other predictors. This dataset was never used for hyperparameter tuning or model training and thus serves as an independent validation set. Envision outperforms other missense variant effect predictors both on large-scale mutagenesis data and on an independent test dataset comprising 2,312 TP53 variants whose effects were measured using a low-throughput approach. Envision combines 21,026 variant effect measurements from nine large-scale experimental mutagenesis datasets, a hitherto untapped training resource, with a supervised, stochastic gradient boosting learning algorithm. Here, we leverage these datasets to develop Envision, which predicts the magnitude of a missense variant's molecular effect. Large datasets describing the quantitative effects of mutations on protein function are becoming increasingly available. Gray, Vanessa E Hause, Ronald J Luebeck, Jens Shendure, Jay Fowler, Douglas M Quantitative Missense Variant Effect Prediction Using Large-Scale Mutagenesis Data. Use of functional assay-based datasets that avoid prior dependencies promises to be valuable for the ongoing development and accurate benchmarking of such tools. Contemporary variant effect prediction tools are unlikely to be as accurate at the general prediction of functional impacts on proteins as reported prior. These results raise concerns about how such algorithms should be employed, particularly in a clinical setting. Benchmarking with the assay-determined datasets UniFun and BRCA1-DMS yielded areas under the receiver operating characteristic curves in the modest ranges of 0.52 to 0.63 and 0.54 to 0.75, respectively, considerably lower than observed for other, potentially more conflicted datasets. Apparent accuracies of variant effect prediction tools were influenced significantly by the benchmarking dataset. Here, we derive three independent, functionally determined human mutation datasets, UniFun, BRCA1-DMS and TP53-TA, and employ them, alongside previously described datasets, to assess the pre-eminent variant effect prediction tools. It is vital that we better understand their accuracies and limitations because published performance metrics are confounded by serious problems of circularity and error propagation. Genetic variant effect prediction algorithms are used extensively in clinical genomics and research to determine the likely consequences of amino acid substitutions on protein function. Mahmood, Khalid Jung, Chol-Hee Philip, Gayle Georgeson, Peter Chung, Jessica Pope, Bernard J Park, Daniel J Variant effect prediction tools assessed using independent, functional assay-based datasets: implications for discovery and diagnostics.
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